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| license: cc-by-4.0 |
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| # GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control |
| CVPR 2025 (Highlight) |
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|
| [Xuanchi Ren*](https://xuanchiren.com/), |
| [Tianchang Shen*](https://www.cs.toronto.edu/~shenti11/) |
| [Jiahui Huang](https://huangjh-pub.github.io/), |
| [Huan Ling](https://www.cs.toronto.edu/~linghuan/), |
| [Yifan Lu](https://yifanlu0227.github.io/), |
| [Merlin Nimier-David](https://merlin.nimierdavid.fr/), |
| [Thomas Müller](https://research.nvidia.com/person/thomas-muller), |
| [Alexander Keller](https://research.nvidia.com/person/alex-keller), |
| [Sanja Fidler](https://www.cs.toronto.edu/~fidler/), |
| [Jun Gao](https://www.cs.toronto.edu/~jungao/) <br> |
| \* indicates equal contribution <br> |
| **[Paper](https://arxiv.org/pdf/2503.03751), [Project Page](https://research.nvidia.com/labs/toronto-ai/GEN3C/)** |
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|
| Abstract: We present GEN3C, a generative video model with precise Camera Control and |
| temporal 3D Consistency. Prior video models already generate realistic videos, |
| but they tend to leverage little 3D information, leading to inconsistencies, |
| such as objects popping in and out of existence. Camera control, if implemented |
| at all, is imprecise, because camera parameters are mere inputs to the neural |
| network which must then infer how the video depends on the camera. In contrast, |
| GEN3C is guided by a 3D cache: point clouds obtained by predicting the |
| pixel-wise depth of seed images or previously generated frames. When generating |
| the next frames, GEN3C is conditioned on the 2D renderings of the 3D cache with |
| the new camera trajectory provided by the user. Crucially, this means that |
| GEN3C neither has to remember what it previously generated nor does it have to |
| infer the image structure from the camera pose. The model, instead, can focus |
| all its generative power on previously unobserved regions, as well as advancing |
| the scene state to the next frame. Our results demonstrate more precise camera |
| control than prior work, as well as state-of-the-art results in sparse-view |
| novel view synthesis, even in challenging settings such as driving scenes and |
| monocular dynamic video. Results are best viewed in videos. |
|
|
| ## Citation |
| ``` |
| @inproceedings{ren2025gen3c, |
| title={GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control}, |
| author={Ren, Xuanchi and Shen, Tianchang and Huang, Jiahui and Ling, Huan and |
| Lu, Yifan and Nimier-David, Merlin and Müller, Thomas and Keller, Alexander and |
| Fidler, Sanja and Gao, Jun}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| year={2025} |
| } |
